import tensorflow as tf
from tensorflow.keras import layers, models

class TransformerTranslator(models.Model):
    def __init__(self, embed_dim, num_heads, ff_dim, vocab_size, maxlen):
        super(TransformerTranslator, self).__init__()
        self.encoder_embedding = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
        self.decoder_embedding = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
        self.encoder = TransformerBlock(embed_dim, num_heads, ff_dim)
        self.decoder = TransformerBlock(embed_dim, num_heads, ff_dim)
        self.final_dense = layers.Dense(vocab_size, activation='softmax')

    def call(self, inputs):
        encoder_input, decoder_input = inputs
        encoder_output = self.encoder(self.encoder_embedding(encoder_input))
        decoder_output = self.decoder(self.decoder_embedding(decoder_input), encoder_output)
        return self.final_dense(decoder_output)

# 示例：机器翻译模型
embed_dim = 64
num_heads = 2
ff_dim = 128
vocab_size = 10000
maxlen = 100

translator = TransformerTranslator(embed_dim, num_heads, ff_dim, vocab_size, maxlen)
translator.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 假设已有双语数据集
# encoder_input = ...
# decoder_input = ...
# decoder_output = ...

# translator.fit([encoder_input, decoder_input], decoder_output, epochs=10, batch_size=64)